Building an AI-First Dashboard with Snowflake Dashboards 2.0

A developer named Sagara built a walkthrough of Snowflake's Dashboards 2.0, an AI-first BI feature in public preview that lets analysts create SQL-based, multi-tile dashboards as .dash files in Snowsight Workspaces by chatting with Cortex Code (CoCo). The walkthrough defines a Semantic View over TPC-H sample data, explains the .dash file structure, and covers role-based sharing, noting that tile queries run with the viewer's privileges and require SELECT only on the Semantic View rather than the underlying tables. The developer also flags that 2.0 is not a drop-in replacement for the legacy version, since the old :filter_name syntax must be replaced with {{ filter('name') }}.

Note: This is the English translation of the following article. https://dev.classmethod.jp/articles/snowflake-try-dashboards-2/ https://dev.classmethod.jp/articles/snowflake-try-dashboards-2/ Hi, I'm Sagara. Snowflake has a new feature called Dashboards 2.0 . It's an AI-first BI artifact. You build a dashboard as a .dash file in Snowsight Workspaces, share it with roles, and viewers open it in CoWork or Snowsight. https://docs.snowflake.com/en/user-guide/snowflake-dashboards https://docs.snowflake.com/en/user-guide/snowflake-dashboards The official docs below cover the sharing details. https://docs.snowflake.com/user-guide/snowflake-artifacts-create-share https://docs.snowflake.com/user-guide/snowflake-artifacts-create-share In this article, I'll define a Semantic View on the TPC-H sample data and then visualize it with Dashboards 2.0. I'll also briefly explain what's inside the generated .dash file and try granting privileges through roles. Note: As of November 13, 2026, the official docs list this feature as "Preview Feature — Open." The specs may change before GA, so please keep that in mind. Dashboards 2.0 is a SQL-based, multi-tile dashboard that you create in Snowsight Workspaces. Analysts build it by chatting with Cortex Code CoCo and then share it with roles. Viewers open it in CoWork or Snowsight and can explore by clicking on charts. In CoWork, viewers can also ask follow-up questions about a tile. A dashboard is mainly made up of these components: | Component | Description | |---|---| | Dashboard | The main body, containing the layout, filters, and tiles the .dash file | | Tile | A single display unit with SQL and a visualization type chart / table / markdown . Placed on a 12-column grid | | Tab | A group of tiles. Up to 20 per dashboard. Filters apply across all tabs | | Filter | Defined per dashboard. Connected to tile SQL with {{ filter 'name' }} | | Hierarchy | A hierarchy definition used for drill-down | | Chart action | Clicking or dragging on a chart filters or highlights other tiles | | Artifact | A read-only copy of the .dash file. This is the unit you share with roles | Tile queries run with the viewer's own privileges caller's rights , and no data is extracted. Even though queries run with the viewer's privileges, a tile that references a Semantic View only needs the viewer to have SELECT on that Semantic View. You don't need to grant privileges on the underlying tables. The privileges required to query a Semantic View are listed on this page of the official docs: https://docs.snowflake.com/en/user-guide/views-semantic/querying https://docs.snowflake.com/en/user-guide/views-semantic/querying The official docs state clearly that Dashboards 2.0 is not a drop-in replacement for the legacy version. The main differences are: | Item | Legacy | Dashboards 2.0 | |---|---|---| | Follow-up questions | Not supported | Conversational in CoWork | | How to create | Worksheet UI | CoCo + manual | | Access control | UBAC | RBAC | | Filter scope | Account-wide | Per dashboard | | Query cache | Shared within a role | Per user results reused per viewer | Version 2.0 doesn't recognize the legacy :filter name syntax. You have to replace it with {{ filter 'name' }} . An upgrade tool can also do this conversion automatically. Pei-san, a Snowflake Data Superhero, wrote a great article in Japanese titled "Why Snowflake Dashboards 2.0 Is the Best, and What BI Will Need Going Forward." It covers the feature's characteristics and where BI is heading. It was a big help when I wrote the overview section of this article. Please check it out as well. https://zenn.dev/dataheroes/articles/snowflake-dashboards-2-and-future-of-bi https://zenn.dev/dataheroes/articles/snowflake-dashboards-2-and-future-of-bi Here are the items from the "Limitations public preview " section of the official docs that seem relevant to this walkthrough: .dash file in Workspaces. Users granted Can edit the WRITE privilege can publish changes to the artifact. Note that having Open source file will point to a path that no longer exists. Renaming or moving the file may make its link to the artifact outdated or break it. If you then create the artifact again, it may become a separate artifact with a different link. {{ hierarchy key ... }} must also use {{ hierarchy rows ... }} . SHOW ARTIFACTS , DESCRIBE ARTIFACT , etc. is listed as "Coming soon" as of November 13, 2026. For this walkthrough, I'll set up an author role and a viewer role. USE ROLE ACCOUNTADMIN; -- Author role and viewer role CREATE ROLE IF NOT EXISTS DASH AUTHOR; CREATE ROLE IF NOT EXISTS DASH VIEWER; -- Database, schema, and warehouse for testing CREATE DATABASE IF NOT EXISTS DASH TEST DB; CREATE SCHEMA IF NOT EXISTS DASH TEST DB.SV; CREATE WAREHOUSE IF NOT EXISTS DASH TEST WH WAREHOUSE SIZE = XSMALL AUTO SUSPEND = 60; -- Create the sample database run only if SNOWFLAKE SAMPLE DATA does not exist in your account CREATE DATABASE SNOWFLAKE SAMPLE DATA FROM SHARE SFC SAMPLES.SAMPLE DATA; Next, grant the required privileges to the author role DASH AUTHOR . -- Privileges on the sample data shared DB - author only; not granted to the viewer GRANT IMPORTED PRIVILEGES ON DATABASE SNOWFLAKE SAMPLE DATA TO ROLE DASH AUTHOR; -- Author role privileges limited to what's needed GRANT USAGE ON DATABASE DASH TEST DB TO ROLE DASH AUTHOR; GRANT USAGE ON SCHEMA DASH TEST DB.SV TO ROLE DASH AUTHOR; GRANT CREATE SEMANTIC VIEW ON SCHEMA DASH TEST DB.SV TO ROLE DASH AUTHOR; GRANT USAGE ON WAREHOUSE DASH TEST WH TO ROLE DASH AUTHOR; -- Privilege to share artifacts don't rely on the default grant to PUBLIC GRANT MANAGE ACCESS ON DASHBOARD ARTIFACTS ON ACCOUNT TO ROLE DASH AUTHOR; -- Grant the author role to your own user GRANT ROLE DASH AUTHOR TO USER <your user ; I'll define a Semantic View using the TPC-H ORDERS , CUSTOMER , and LINEITEM tables. USE ROLE DASH AUTHOR; USE WAREHOUSE DASH TEST WH; USE SCHEMA DASH TEST DB.SV; CREATE OR REPLACE SEMANTIC VIEW TPCH SALES SV TABLES customers AS SNOWFLAKE SAMPLE DATA.TPCH SF1.CUSTOMER PRIMARY KEY c custkey COMMENT = 'Customers', orders AS SNOWFLAKE SAMPLE DATA.TPCH SF1.ORDERS PRIMARY KEY o orderkey COMMENT = 'Orders', line items AS SNOWFLAKE SAMPLE DATA.TPCH SF1.LINEITEM PRIMARY KEY l orderkey, l linenumber COMMENT = 'Order line items' RELATIONSHIPS orders to customers AS orders o custkey REFERENCES customers, line items to orders AS line items l orderkey REFERENCES orders FACTS line items.net price AS l extendedprice 1 - l discount DIMENSIONS customers.customer name AS c name, customers.market segment AS c mktsegment, orders.order date AS o orderdate, orders.order year AS YEAR o orderdate , orders.order priority AS o orderpriority METRICS orders.order count AS COUNT o orderkey , line items.total net sales AS SUM line items.net price COMMENT = 'Semantic View for TPC-H sales analysis'; I based the DDL syntax on this page of the official docs: https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view To confirm the Semantic View was created, query it directly run these as the DASH AUTHOR role . -- Total sales by year SELECT FROM SEMANTIC VIEW DASH TEST DB.SV.TPCH SALES SV DIMENSIONS orders.order year METRICS line items.total net sales ORDER BY order year; -- Order count by order priority SELECT FROM SEMANTIC VIEW DASH TEST DB.SV.TPCH SALES SV DIMENSIONS orders.order priority METRICS orders.order count ORDER BY order priority; If you get one row per year and one row per priority, you're good. I based the SEMANTIC VIEW clause syntax on this page of the official docs: Note that the sales metric total net sales is the sum of each line item's extended price l extendedprice minus its discount. It won't match amounts that include tax or the sum of ORDERS.O TOTALPRICE . Open Workspaces in Snowsight and switch the role to DASH AUTHOR and the warehouse to DASH TEST WH in the Workspace. Then select Add new → Dashboard . The CoCo chat panel opens on the right side of the Workspace. In the prompt, I specify which Semantic View to use. If you don't specify one, CoCo searches for a relevant Semantic View, but this time I named it explicitly. I also told CoCo not to query the underlying tables directly and not to redefine the metrics on its own. This keeps the dashboard consistent with the setup, where the viewer has no privileges on the underlying tables. /dashboard Using DASH TEST DB.SV.TPCH SALES SV, please create a TPC-H sales dashboard. - Total sales trend by year line chart - Total sales by market segment bar chart - Order count by order priority table - Filter: order year All tile SQL, and any SQL used to fetch the options for the order year filter, must reference DASH TEST DB.SV.TPCH SALES SV. Do not reference the underlying tables in SNOWFLAKE SAMPLE DATA directly. Use line items.total net sales for sales and orders.order count for order count, and do not redefine the metrics on your own. The prompt in the screenshot is the original Japanese version. CoCo shows this as in the screenshot below and starts by drafting an implementation plan. For some reason it wrote the plan in English, so I asked it to reply in Japanese. The implementation plan then appeared in Japanese. It looked fine, so I asked CoCo to build it. After a short wait, a .dash file was created in Workspaces and opened, with charts showing in the tiles. Next, I asked CoCo to add a chart action. Chart actions can't be added from the tile editor; they can only be added through CoCo. I sent CoCo the following request: When a bar in the sales by market segment chart is clicked, add a chart action that filters the sales by year and the order count by order priority to the selected market segment. As a result, a chart action was added: clicking a bar in "Total Sales by Market Segment" now updates the other tiles, as shown below. By the way, here's what the definition of the added filter looked like. You can also add filters manually through the GUI. However, the GUI didn't let me pick a Semantic View dimension, so the filter uses a SELECT statement that extracts a single dimension from the Semantic View. Let's open the generated .dash file and look at its contents. You can view it by clicking View JSON in the top-right corner of the dashboard. The file is in JSON format. The file generated this time is made up of the following main blocks. The full file is included in a collapsible section at the end of this step. | Key | Contents | |---|---| | version / spec version / spec revision | Format version information 2 / 2 / 17 in this case | | title | Dashboard title | | filters | Filter definitions order year , market segment | | datasources | SQL definitions used by tiles and filter options | | tabs | Tabs and the tiles charts and tables inside them | | layout | Position and size of each tile on the 12-column grid | | scopes | Definitions of how far chart actions reach | filters Order year is defined as single select , and market segment as multi select . The options use "source": "query" and reference a datasource that fetches them such as ds-order year-options . The official docs write filter modes with hyphens, like multi-select , but the actual file uses underscores, like multi select . datasources Each datasource is "type": "sql" , with the SQL embedded as a string. For example, the SQL for sales by year looked like this: SELECT order year, AGG total net sales AS TOTAL NET SALES FROM DASH TEST DB.SV.TPCH SALES SV WHERE {{ filter 'order year' }} AND {{ filter 'market segment' }} GROUP BY order year ORDER BY order year Key points: FROM and aggregates metrics with AGG . TPCH SALES SV and none touched the underlying tables in SNOWFLAKE SAMPLE DATA directly. Sales used total net sales , order count used order count , and neither metric was redefined. So the instructions from Step 1 were followed. {{ filter 'order year' }} in the SQL and the filter id in filter bindings . ds-sales-by-segment is bound only to order year ; the market segment filter isn't connected. My guess is that this is because this tile is the source of the chart action. ds-order year-options use SQL that extracts a single dimension from the Semantic View with GROUP BY , as mentioned in Step 2. tabs There are two tile types, chart and table . A chart tile's spec uses mark line / bar and encoding field , type , and title for x / y . The official docs say chart tiles are rendered with Vega-Lite, and this format is indeed close to Vega-Lite syntax. The chart action added in Step 2 is defined as interactions on the sales by market segment tile. "interactions": { "trigger": "select", "operator": "set filter", "fields": "MARKET SEGMENT" , "targets": { "scope": "segment action", "fields": "MARKET SEGMENT" , "filter": "market segment" } } When a bar is selected select , this sets the market segment filter set filter . The target tiles sales by year and order count by order priority have "scope": "segment action" , and segment action is defined in scopes at the end of the file. layout layout specifies x / y / w / h for each tile, per tab. Sales by year has a width of 12 full width . Sales by market segment and order count by order priority have a width of 6 each and sit side by side in the bottom row. This matches the "12-column grid" described in the official docs. { "version": 2, "title": "TPC-H 売上ダッシュボード", "filters": { "order year": { "label": "注文年", "mode": "single select", "valueType": "STRING", "options": { "source": "query", "datasources": "ds-order year-options" }, "default values": }, "market segment": { "label": "市場セグメント", "mode": "multi select", "valueType": "STRING", "options": { "source": "query", "datasources": "ds-market segment-options" }, "default values": } }, "datasources": { "ds-order year-options": { "type": "sql", "sql": "SELECT\n order year\nFROM DASH TEST DB.SV.TPCH SALES SV\nGROUP BY\n order year\nORDER BY\n order year" }, "ds-sales-by-year": { "type": "sql", "columns": { "name": "ORDER YEAR", "type": "NUMBER" }, { "name": "TOTAL NET SALES", "type": "NUMBER" } , "sql": "SELECT\n order year,\n AGG total net sales AS TOTAL NET SALES\nFROM DASH TEST DB.SV.TPCH SALES SV\nWHERE\n {{ filter 'order year' }}\n AND {{ filter 'market segment' }}\nGROUP BY\n order year\nORDER BY\n order year", "filter bindings": { "filter id": "order year" }, { "filter id": "market segment" } }, "ds-sales-by-segment": { "type": "sql", "columns": { "name": "MARKET SEGMENT", "type": "VARCHAR" }, { "name": "TOTAL NET SALES", "type": "NUMBER" } , "sql": "SELECT\n market segment,\n AGG total net sales AS TOTAL NET SALES\nFROM DASH TEST DB.SV.TPCH SALES SV\nWHERE\n {{ filter 'order year' }}\nGROUP BY\n market segment\nORDER BY\n TOTAL NET SALES DESC", "filter bindings": { "filter id": "order year" } }, "ds-orders-by-priority": { "type": "sql", "columns": { "name": "ORDER PRIORITY", "type": "VARCHAR" }, { "name": "ORDER COUNT", "type": "NUMBER" } , "sql": "SELECT\n order priority,\n AGG order count AS ORDER COUNT\nFROM DASH TEST DB.SV.TPCH SALES SV\nWHERE\n {{ filter 'order year' }}\n AND {{ filter 'market segment' }}\nGROUP BY\n order priority\nORDER BY\n order priority", "filter bindings": { "filter id": "order year" }, { "filter id": "market segment" } }, "ds-market segment-options": { "type": "sql", "sql": "SELECT\n market segment\nFROM DASH TEST DB.SV.TPCH SALES SV\nGROUP BY\n market segment\nORDER BY\n market segment" } }, "tabs": { "tab id": "tab-0", "title": "概要", "tiles": { "tile id": "sales-by-year", "type": "chart", "title": "年別 売上合計の推移", "datasources": "ds-sales-by-year" , "spec": { "mark": "line", "encoding": { "x": { "field": "ORDER YEAR", "type": "ordinal", "title": "注文年" }, "y": { "field": "TOTAL NET SALES", "type": "quantitative", "title": "売上合計" } } }, "scope": "segment action" }, { "tile id": "sales-by-segment", "type": "chart", "title": "市場セグメント別 売上合計", "datasources": "ds-sales-by-segment" , "spec": { "mark": "bar", "encoding": { "x": { "field": "MARKET SEGMENT", "type": "nominal", "title": "市場セグメント", "sort": "-y" }, "y": { "field": "TOTAL NET SALES", "type": "quantitative", "title": "売上合計" } } }, "interactions": { "trigger": "select", "operator": "set filter", "fields": "MARKET SEGMENT" , "targets": { "scope": "segment action", "fields": "MARKET SEGMENT" , "filter": "market segment" } } }, { "tile id": "orders-by-priority", "type": "table", "title": "注文優先度別 注文件数", "datasources": "ds-orders-by-priority" , "spec": { "columns": { "field": "ORDER PRIORITY" }, { "field": "ORDER COUNT" } }, "scope": "segment action" } } , "layout": { "tab-0": { "meta": { "default": true }, "positions": { "sales-by-year": { "x": 0, "y": 0, "w": 12, "h": 4 }, "sales-by-segment": { "x": 0, "y": 4, "w": 6, "h": 4 }, "orders-by-priority": { "x": 6, "y": 4, "w": 6, "h": 4 } } } }, "spec version": 2, "spec revision": 17, "scopes": { "segment action": {} } } To show the dashboard to other users, you share it as an artifact. First, as an administrator, set up the privileges for the viewer role. Viewer queries run with the viewer's own privileges, but when they reference a Semantic View, only SELECT on the Semantic View is needed. We don't grant any privileges on the underlying tables SNOWFLAKE SAMPLE DATA . USE ROLE ACCOUNTADMIN; -- Privileges required for the viewer role no privileges on the underlying tables GRANT USAGE ON WAREHOUSE DASH TEST WH TO ROLE DASH VIEWER; GRANT USAGE ON DATABASE DASH TEST DB TO ROLE DASH VIEWER; GRANT USAGE ON SCHEMA DASH TEST DB.SV TO ROLE DASH VIEWER; GRANT SELECT ON SEMANTIC VIEW DASH TEST DB.SV.TPCH SALES SV TO ROLE DASH VIEWER; Next, in Snowsight, with the role set to DASH AUTHOR and the warehouse set to DASH TEST WH , open the .dash file in Workspaces and select Publish artifact . In the dialog that opens, do the following: Who has access , add the DASH VIEWER role. Can view . Publish . When you click Publish , the artifact link is copied to your clipboard. You can find published dashboards under Workspaces → Artifacts in Snowsight. How the artifact relates to the original .dash file, and how updates work A shared artifact is stored separately from the original .dash file. https://docs.snowflake.com/en/user-guide/snowflake-artifacts-create-share https://docs.snowflake.com/en/user-guide/snowflake-artifacts-create-share An artifact is a securable object identified by a UUID. The UUID appears after /artifacts/ in the artifact's URL. You can grant three types of privileges: | Privilege | Description | |---|---| | OWNERSHIP | Delete and transfer ownership | | READ | View the contents | | WRITE | View and modify including publishing changes to the artifact | If you already granted Can view to DASH VIEWER through the UI earlier, the SQL below isn't a required step. I'm including it as an example of how to grant privileges with SQL , run by an administrator ACCOUNTADMIN . USE ROLE ACCOUNTADMIN; -- Example: grant READ to the viewer role GRANT READ ON ARTIFACT "<artifact UUID " TO ROLE DASH VIEWER; SQL for checking artifacts, such as SHOW ARTIFACTS and DESCRIBE ARTIFACT , is listed as "Coming soon" as of November 13, 2026. So this article doesn't check privileges with SQL. The user who created the dashboard owns the artifact, so I checked viewer access with a different user. As an administrator, grant the viewer role to an existing test user. USE ROLE ACCOUNTADMIN; GRANT ROLE DASH VIEWER TO USER <viewer user ; This assumes the test user doesn't have DASH AUTHOR or any administrator role. I logged in to Snowsight as this user and opened Workspaces → Artifacts . The dashboard was viewable, as shown below. I tried Snowflake Dashboards 2.0 together with a Semantic View and confirmed that sharing through roles works. It's an AI-first, code-based dashboard, and I found it very easy to use. Give it a try